We investigate the perceived quality of natural images. To do so, we linearly scale the luminance range of high dynamic range images to generate a set of tone-mapped images that cover the full range of mean-luminance and contrast values that a CRT monitor can display. Image patches are displayed on a uniform black, grey or white background and subjects are asked to evaluate the quality of each image on a 0-9 scale. We find that image quality scores can be predicted using a three-stage model: First, luminance is converted to lightness using an expansive power-law that varies with the background luminance (γblack=0.3, γgray=0.35, γwhite=0.45). Second, the standard deviation of the gamma-adjusted, lightness image is computed. Third, the standard deviation is passed through an expansive power-law (γ=0.3) to estimate the perceived contrast of an image. This metric can accurately predict the average image quality scores over the full range of onscreen luminance and contrast values investigated (r=0.94, p<0.0001). A second investigation reveals that the proposed contrast metric is linearly related to image quality scores for all test images with a mean Pearson's correlation of 0.87 (N=128), however the slope of the function varies substantially between images and we are unable to model this effect. The proposed contrast metric is able to predict the perceived quality of tone-mapped images in the database of Cadik et al. (2008) despite the existence of a wide variety of image artefacts (ringing, colour distortions, ect) in the image set (r=0.85, p<0.0001). Finally, we note that the proposed super-threshold contrast metric performs histogram equalisation on the luminance distribution and removes skew from the contrast distribution of natural scenes, suggesting an optimal coding strategy.